Cross-material catalyst discovery via deep learning

Abstract

The discovery of catalysts is typically confined within individual material classes, limiting insight from across material types. Here we demonstrate a machine learning approach that bridges catalyst families by identifying co-descriptors derived from two experimental datasets: single-atom catalysts (SACs) on carbon and bulk perovskite oxides. This co-descriptor set, selected through automated statistical and natural-language analyses, enabled integration of distinct experimental catalyst datasets by yielding shared activity-related chemical features. The resulting unified model, the crossbreeding neural network (CBNN), enables prediction of oxygen evolution activity in a previously untrained class—SACs on perovskite oxides. The CBNN precisely predicted performance trends of experimentally synthesized catalysts by overpotential, including a multimetallic catalyst with superior activity compared with all previous candidates. Explainable machine learning further connected descriptor importance and surface atomic contributions to activity trends. These results suggest that cross-material machine learning can accelerate the discovery of high-performance catalysts beyond known design spaces.